Classification Code
Classification code research focuses on developing and improving algorithms and models to accurately assign data points to predefined categories. Current efforts concentrate on addressing challenges like imbalanced datasets, noisy data, and limited labeled data through techniques such as self-supervised pre-training, robust loss functions, and the application of diverse architectures including convolutional neural networks (CNNs), transformers, and novel approaches like Mamba. These advancements have significant implications across various fields, improving accuracy and efficiency in applications ranging from medical image analysis and bioacoustic monitoring to cybersecurity threat detection and scientific literature organization.
Papers
Parallel and Streaming Wavelet Neural Networks for Classification and Regression under Apache Spark
Eduru Harindra Venkatesh, Yelleti Vivek, Vadlamani Ravi, Orsu Shiva Shankar
Multi-Scale Attention-based Multiple Instance Learning for Classification of Multi-Gigapixel Histology Images
Made Satria Wibawa, Kwok-Wai Lo, Lawrence Young, Nasir Rajpoot
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure
Phoebe M Asquith, Hisham Ihshaish
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning
Umang Goenka, Param Patil, Kush Gosalia, Aaryan Jagetia
Robustness of an Artificial Intelligence Solution for Diagnosis of Normal Chest X-Rays
Tom Dyer, Jordan Smith, Gaetan Dissez, Nicole Tay, Qaiser Malik, Tom Naunton Morgan, Paul Williams, Liliana Garcia-Mondragon, George Pearse, Simon Rasalingham